
'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification







def 	A  (   UpperCamelCase_       :  int   )       ->							Optional[Any]:







			'''simple docstring'''



			lowerCAmelCase__										=				SwinvaConfig()
			lowerCAmelCase__										=				swinva_name.split("_"   )

			lowerCAmelCase__										=				name_split[1]
			if "to" in name_split[3]:
						lowerCAmelCase__										=				int(name_split[3][-3:]   )
			else:
						lowerCAmelCase__										=				int(name_split[3]   )
			if "to" in name_split[2]:
						lowerCAmelCase__										=				int(name_split[2][-2:]   )
			else:
						lowerCAmelCase__										=				int(name_split[2][6:]   )

			if model_size == "tiny":
						lowerCAmelCase__										=				96
						lowerCAmelCase__										=				(2, 2, 6, 2)
						lowerCAmelCase__										=				(3, 6, 12, 24)
			elif model_size == "small":
						lowerCAmelCase__										=				96
						lowerCAmelCase__										=				(2, 2, 18, 2)
						lowerCAmelCase__										=				(3, 6, 12, 24)
			elif model_size == "base":
						lowerCAmelCase__										=				1_28
						lowerCAmelCase__										=				(2, 2, 18, 2)
						lowerCAmelCase__										=				(4, 8, 16, 32)
			else:
						lowerCAmelCase__										=				1_92
						lowerCAmelCase__										=				(2, 2, 18, 2)
						lowerCAmelCase__										=				(6, 12, 24, 48)

			if "to" in swinva_name:
						lowerCAmelCase__										=				(12, 12, 12, 6)

			if ("22k" in swinva_name) and ("to" not in swinva_name):
						lowerCAmelCase__										=				2_18_41
						lowerCAmelCase__										=				"huggingface/label-files"
						lowerCAmelCase__										=				"imagenet-22k-id2label.json"
						lowerCAmelCase__										=				json.load(open(hf_hub_download(UpperCamelCase_    ,						UpperCamelCase_    ,						repo_type="dataset"   )    ,						"r"   )   )
						lowerCAmelCase__										=				{int(UpperCamelCase_   ): v for k, v in idalabel.items()}
						lowerCAmelCase__										=				idalabel
						lowerCAmelCase__										=				{v: k for k, v in idalabel.items()}

			else:
						lowerCAmelCase__										=				10_00
						lowerCAmelCase__										=				"huggingface/label-files"
						lowerCAmelCase__										=				"imagenet-1k-id2label.json"
						lowerCAmelCase__										=				json.load(open(hf_hub_download(UpperCamelCase_    ,						UpperCamelCase_    ,						repo_type="dataset"   )    ,						"r"   )   )
						lowerCAmelCase__										=				{int(UpperCamelCase_   ): v for k, v in idalabel.items()}
						lowerCAmelCase__										=				idalabel
						lowerCAmelCase__										=				{v: k for k, v in idalabel.items()}

			lowerCAmelCase__										=				img_size
			lowerCAmelCase__										=				num_classes
			lowerCAmelCase__										=				embed_dim
			lowerCAmelCase__										=				depths
			lowerCAmelCase__										=				num_heads
			lowerCAmelCase__										=				window_size

			return config







def 	A  (   UpperCamelCase_       :  Dict   )       ->							int:







			'''simple docstring'''



			if "patch_embed.proj" in name:
						lowerCAmelCase__										=				name.replace("patch_embed.proj"    ,						"embeddings.patch_embeddings.projection"   )
			if "patch_embed.norm" in name:
						lowerCAmelCase__										=				name.replace("patch_embed.norm"    ,						"embeddings.norm"   )
			if "layers" in name:
						lowerCAmelCase__										=				"encoder." + name
			if "attn.proj" in name:
						lowerCAmelCase__										=				name.replace("attn.proj"    ,						"attention.output.dense"   )
			if "attn" in name:
						lowerCAmelCase__										=				name.replace("attn"    ,						"attention.self"   )
			if "norm1" in name:
						lowerCAmelCase__										=				name.replace("norm1"    ,						"layernorm_before"   )
			if "norm2" in name:
						lowerCAmelCase__										=				name.replace("norm2"    ,						"layernorm_after"   )
			if "mlp.fc1" in name:
						lowerCAmelCase__										=				name.replace("mlp.fc1"    ,						"intermediate.dense"   )
			if "mlp.fc2" in name:
						lowerCAmelCase__										=				name.replace("mlp.fc2"    ,						"output.dense"   )
			if "q_bias" in name:
						lowerCAmelCase__										=				name.replace("q_bias"    ,						"query.bias"   )
			if "k_bias" in name:
						lowerCAmelCase__										=				name.replace("k_bias"    ,						"key.bias"   )
			if "v_bias" in name:
						lowerCAmelCase__										=				name.replace("v_bias"    ,						"value.bias"   )
			if "cpb_mlp" in name:
						lowerCAmelCase__										=				name.replace("cpb_mlp"    ,						"continuous_position_bias_mlp"   )
			if name == "norm.weight":
						lowerCAmelCase__										=				"layernorm.weight"
			if name == "norm.bias":
						lowerCAmelCase__										=				"layernorm.bias"

			if "head" in name:
						lowerCAmelCase__										=				name.replace("head"    ,						"classifier"   )
			else:
						lowerCAmelCase__										=				"swinv2." + name

			return name







def 	A  (   UpperCamelCase_       :  List[Any]    ,						UpperCamelCase_       :  Optional[int]   )       ->							List[str]:







			'''simple docstring'''



			for key in orig_state_dict.copy().keys():
						lowerCAmelCase__										=				orig_state_dict.pop(UpperCamelCase_   )

						if "mask" in key:
									continue
						elif "qkv" in key:
									lowerCAmelCase__										=				key.split("."   )
									lowerCAmelCase__										=				int(key_split[1]   )
									lowerCAmelCase__										=				int(key_split[3]   )
									lowerCAmelCase__										=				model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

									if "weight" in key:
												lowerCAmelCase__										=				val[:dim, :]
												lowerCAmelCase__										=				val[dim : dim * 2, :]
												lowerCAmelCase__										=				val[-dim:, :]
									else:
												lowerCAmelCase__										=				val[:dim]
												lowerCAmelCase__										=				val[
												    dim : dim * 2
												]
												lowerCAmelCase__										=				val[-dim:]
						else:
									lowerCAmelCase__										=				val

			return orig_state_dict







def 	A  (   UpperCamelCase_       :  Optional[int]    ,						UpperCamelCase_       :  Any   )       ->							Optional[Any]:







			'''simple docstring'''



			lowerCAmelCase__										=				timm.create_model(UpperCamelCase_    ,						pretrained=UpperCamelCase_   )
			timm_model.eval()

			lowerCAmelCase__										=				get_swinva_config(UpperCamelCase_   )
			lowerCAmelCase__										=				SwinvaForImageClassification(UpperCamelCase_   )
			model.eval()

			lowerCAmelCase__										=				convert_state_dict(timm_model.state_dict()    ,						UpperCamelCase_   )
			model.load_state_dict(UpperCamelCase_   )

			lowerCAmelCase__										=				"http://images.cocodataset.org/val2017/000000039769.jpg"

			lowerCAmelCase__										=				AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"    ,						"-"   )   )   )
			lowerCAmelCase__										=				Image.open(requests.get(UpperCamelCase_    ,						stream=UpperCamelCase_   ).raw   )
			lowerCAmelCase__										=				image_processor(images=UpperCamelCase_    ,						return_tensors="pt"   )

			lowerCAmelCase__										=				timm_model(inputs["pixel_values"]   )
			lowerCAmelCase__										=				model(**UpperCamelCase_   ).logits

			assert torch.allclose(UpperCamelCase_    ,						UpperCamelCase_    ,						atol=1E-3   )

			print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""   )
			model.save_pretrained(UpperCamelCase_   )

			print(F"""Saving image processor to {pytorch_dump_folder_path}"""   )
			image_processor.save_pretrained(UpperCamelCase_   )

			model.push_to_hub(
			    repo_path_or_name=Path(UpperCamelCase_    ,						UpperCamelCase_   )    ,						organization="nandwalritik"    ,						commit_message="Add model"    ,						)


if __name__ == "__main__":
		UpperCAmelCase__ :			int											=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--swinv2_name",
		    default="swinv2_tiny_patch4_window8_256",
		    type=str,
		    help="Name of the Swinv2 timm model you'd like to convert.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
		)

		UpperCAmelCase__ :			Tuple											=		parser.parse_args()
		convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)












'''simple docstring'''






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 		lowerCamelCase	(   _A							):
	snake_case_   = ["image_processor", "tokenizer"]
	snake_case_   = "LayoutLMv3ImageProcessor"
	snake_case_   = ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")



	def __init__( self		,   a_=None		,   a_=None		,   **a_						):
			lowerCAmelCase  :     Union[str, Any]       						= None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
					    " instead."		,   a_		,   )
					lowerCAmelCase  :     Tuple       						= kwargs.pop("feature_extractor"						)

			lowerCAmelCase  :     str       						= image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
					raise ValueError("You need to specify an `image_processor`."						)
			if tokenizer is None:
					raise ValueError("You need to specify a `tokenizer`."						)

			super().__init__(a_		,   a_						)



	def __call__( self		,   a_		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = True		,   a_ = False		,   a_ = None		,   a_ = None		,   a_ = 0		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = False		,   a_ = False		,   a_ = False		,   a_ = False		,   a_ = True		,   a_ = None		,   **a_		,   ):
			# verify input
			if self.image_processor.apply_ocr and (boxes is not None):
					raise ValueError(
					    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."						)

			if self.image_processor.apply_ocr and (word_labels is not None):
					raise ValueError(
					    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."						)

			# first, apply the image processor
			lowerCAmelCase  :     str       						= self.image_processor(images=a_		,   return_tensors=a_						)

			# second, apply the tokenizer
			if text is not None and self.image_processor.apply_ocr and text_pair is None:
					if isinstance(a_		,   a_						):
							lowerCAmelCase  :     Union[str, Any]       						= [text]  # add batch dimension (as the image processor always adds a batch dimension)
					lowerCAmelCase  :     int       						= features["words"]

			lowerCAmelCase  :     List[Any]       						= self.tokenizer(
			    text=text if text is not None else features["words"]		,   text_pair=text_pair if text_pair is not None else None		,   boxes=boxes if boxes is not None else features["boxes"]		,   word_labels=a_		,   add_special_tokens=a_		,   padding=a_		,   truncation=a_		,   max_length=a_		,   stride=a_		,   pad_to_multiple_of=a_		,   return_token_type_ids=a_		,   return_attention_mask=a_		,   return_overflowing_tokens=a_		,   return_special_tokens_mask=a_		,   return_offsets_mapping=a_		,   return_length=a_		,   verbose=a_		,   return_tensors=a_		,   **a_		,   )

			# add pixel values
			lowerCAmelCase  :     Tuple       						= features.pop("pixel_values"						)
			if return_overflowing_tokens is True:
					lowerCAmelCase  :     Optional[int]       						= self.get_overflowing_images(a_		,   encoded_inputs["overflow_to_sample_mapping"]						)
			lowerCAmelCase  :     str       						= images

			return encoded_inputs



	def 	_lowerCamelCase     ( self		,   a_		,   a_						):
			# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
			lowerCAmelCase  :     Optional[Any]       						= []
			for sample_idx in overflow_to_sample_mapping:
					images_with_overflow.append(images[sample_idx]						)

			if len(a_						) != len(a_						):
					raise ValueError(
					    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
					    F''' {len(a_						)} and {len(a_						)}'''						)

			return images_with_overflow



	def 	_lowerCamelCase     ( self		,   *a_		,   **a_						):
			return self.tokenizer.batch_decode(*a_		,   **a_						)



	def 	_lowerCamelCase     ( self		,   *a_		,   **a_						):
			return self.tokenizer.decode(*a_		,   **a_						)



	@property
	def 	_lowerCamelCase     ( self						):
			return ["input_ids", "bbox", "attention_mask", "pixel_values"]



	@property
	def 	_lowerCamelCase     ( self						):
			warnings.warn(
			    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,   a_		,   )
			return self.image_processor_class





	@property
	def 	_lowerCamelCase     ( self						):
			warnings.warn(
			    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,   a_		,   )
			return self.image_processor



